An Image Sharpness Enhancement Algorithm Based on Green Function

The traditional image sharpness enhancement algorithm faces several defects, namely, the lack of details, and the poor subjective effect. To solve these defects, this paper proposes an image sharpness enhancement algorithm based on the Green function. Specifically, the Retinex model was employed to ensure that the enhanced image has outstanding details, and the Poisson’s equation was solved to maintain the consistency between the enhanced image and the original image in the gradient domain. Then, adaptive brightness mapping was carried out to determine the boundary conditions suitable for display, and the boundary of the region was sampled to reduce the complexity of our algorithm. Experimental results show that our algorithm improved the contrast and sharpness of images from the levels of contrastive image enhancement algorithms.

Cross-Based Adaptive Guided Filtering

Edge-preserving and structure-preserving smoothing filtering has attracted much interest in the last decades. A conventional linear filter effectively smoothens noise in homogeneous regions but blurs the edges of an image. This study aimed to present an adaptive guided filter using a cross-based framework. The proposed method outperformed many other algorithms in terms of sharpness enhancement and noise reduction. Moreover, the cross-based adaptive guided filter had a fast and nonapproximate linear-time algorithm as the guided filter.